Evaluation of the use of adsorbent materials in the removal of nitrogen compounds from gas oil as a pre‐treatment for feeds for fluid catalytic cracking units
Bibliographic record
Abstract
Abstract This study was designed to investigate the use of commercial adsorbent materials for the removal of nitrogen compounds from a vacuum gas oil obtained from an industrial atmospheric distillation unit. Two types of adsorbents were tested: a clay developed specifically for the removal of nitrogen compounds from middle distillates (jet fuel and diesel); and a silica used in a variety of industries. Kinetic and thermodynamic equilibrium experiments were conducted at three temperatures: 80, 100, and 120 °C. The variation in the concentration of nitrogen and aromatic compounds was monitored throughout the kinetic adsorption and thermodynamic equilibrium experiments. When an adsorbent/gas oil mass ratio of 0.75 was used, the clay removed around 70 % of the basic nitrogen compounds from the gas oil, while the silica removed 80 % of the same compounds, which are the ones that effectively hamper catalytic cracking. The silica also removed 14.2 % of the aromatic compounds, while the clay only removed 4.1 %. This study shows that it is possible to treat a viscous hydrocarbon feed using an adsorption process to remove nitrogen compounds without the need to dilute the feed. Using a fluidized bed advanced cracking evaluation (ACE) unit, which simulates a fluid catalytic cracking unit on a bench scale, the gas oil treated with silica produced 3 % more liquid petroleum gas (LPG) and 4 % more gasoline, while the gas oil treated with clay produced 2 % more LPG and 3 % more gasoline than the untreated gas oil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".